University of Nebraska - Lincoln University of Nebraska - Lincoln

DigitalCommons@University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln

Library Philosophy and Practice (e-journal) Libraries at University of Nebraska-Lincoln

Winter 1-14-2021

Effect of Information Technology Capital: Technology Effect of Information Technology Capital: Technology

Infrastructure, Database, Software, and Brainware Toward Infrastructure, Database, Software, and Brainware Toward

Optimize the Use of Information Technology (Case Study : UIN Optimize the Use of Information Technology (Case Study : UIN

Sunan Ampel Of Surabaya) Sunan Ampel Of Surabaya)

Rismawati Br Sitepu Airlangga University of Surabaya, East Java East Java, Indonesia, rismasitepu07@gmail.com

Ilham m.said UIN Sunan Ampel Of Surabaya, East Java, Indonesia, ilham-2019@feb.unair.ac.id

Tanti Handriana Airlangga University of Surabaya, East Java East Java, Indonesia

Praptini Yulianti Airlangga University of Surabaya, East Java East Java, Indonesia

Follow this and additional works at: https://digitalcommons.unl.edu/libphilprac

Part of the Business Administration, Management, and Operations Commons, Business Analytics

Commons, Business and Corporate Communications Commons, Corporate Finance Commons, Data

Storage Systems Commons, Library and Information Science Commons, Management Information

Systems Commons, and the Technology and Innovation Commons

Sitepu, Rismawati Br; m.said, Ilham; Handriana, Tanti; and Yulianti, Praptini, "Effect of Information Technology Capital: Technology Infrastructure, Database, Software, and Brainware Toward Optimize the Use of Information Technology (Case Study : UIN Sunan Ampel Of Surabaya)" (2021). Library Philosophy and Practice (e-journal). 4949. https://digitalcommons.unl.edu/libphilprac/4949

Effect of Information Technology Capital: Technology Infrastructure,

Database, Software, and Brainware Toward Optimize the Use of Information

Technology (Case Study : UIN Sunan Ampel Of Surabaya)

Ilham1, Tanti Handriana2, Praptini Yulianti3, Rina Dewi4, Rismawati Br Sitepu5

1Information Systems Study Program, Department of Technology, Faculty of Science and

Technology, Sunan Ampel Islamic University Of Surabaya, East Java, Indonesia

Ahmad Yani Number 117 Street, Surabaya, East Java, Indonesia 1ilham-2019@feb.unair.ac.id

1,2,3,4,5Department of Management, Faculty of Economics and Business, Airlangga University

Airlangga Number 4-6 Street, Surabaya, East Java East Java, Indonesia

ABSTRACT

This research was conducted to determine the extent of the influence of technology infrastructure

costs, software costs, database costs and brainware costs to increase the information technology

budget of the Sunan Ampel State Islamic University in Surabaya and efficient use of the budget.

The purpose of this study is to prove that there is a positive and significant influence of technology

infrastructure costs, software costs, database costs and brainware costs to increase information

technology budgets by using validity and reliability tests and classic tests such as the Normality

test, Multicollinearity test, autocorrelation test, Heteroskedasticity test , and Linearity test. This

study will provide the results of the analysis of each test using Skewness and Kurtosis,

Multicollinearity test with variance inflation factor (VIF), autocorrelation test with Durbin-Watson

test, Heteroskedasticity test with the Glejser test, Linearity test with Durbin-Watson, The

conclusion of the study is that the cost of technology infrastructure has a significant effect, the cost

of software has a significant effect on increasing the information technology budget, database costs

have a significant effect on increasing the information technology budget and the cost of brainware

has a significant effect on increasing the information technology budget.

Keyword : Information Tehnology, Infrastructure, Software, Database, Brainware, Capital, Optimize, and

University

INTRODUCTION

Information technology has changed the map of world power which covers the fields of

geography, economy, politics, social, culture, to the level of defense and security. Along with the

demands of rapid technological development, the government of the Republic of Indonesia has

responded by issuing Presidential Instruction No. 3 of 2003 concerning National Policies and

Strategies for E-Government Development. On the other hand there are strong community

demands for efforts to improve public services and good governance so that the term good

governance appears. Good governance is an aggregate of individual behavior that obeys and

complies with the provisions (regulatory) that have been set, so that good governance is a reflection

of government actors in accordance with predetermined rules [4]. Higher Education has allocated

large amounts of funds for investment in information technology. To support the accountability of

tertiary institutions in the implementation of information technology, a model in the form of

guidelines for monitoring the implementation of information technology is needed. The

development of technology is very fast, especially in developing countries along with the

development of information technology and technology (Khadaroo, 2005).

The first choice for users to use the internet to obtain information is time and makes it easy to

get the desired documents. The main media used by investors and financial analysts is the internet

to obtain financial and non-financial information. This study discusses the classic problems in

managing the budget, which occur in various fields one of them in the world of education.

Although the government has provided various systems for budget management, internal budget

management is still needed. Starting from the submission of the budget plan of each unit, then

merging the data in one satker, submitting revisions and submitting the realization and reporting

as well as statistical monitoring by the leaders of the Higher Education. So in this study created a

system for managing internal budgets for information technology procurement. The results of this

study create an evaluation and recommendation results that can help budget management of

information technology procurement at the Sunan Ampel State Islamic University in Surabaya,

this will be very helpful especially for state universities.

METHOD

The research model in this study is as follows. Increased Technology Budget = α0 + α1

Infrastructure + α2 Software + α3 Databases + α4Brainware + ε. The population in this study was

the Sunan Ampel State Islamic University of Surabaya in 1980-2019. The sample selection method

uses random sampling with the following criteria. a. University Units, Faculties, Departments and

Study Programs that have used information technology and had technological infrastructure in

1980 such as computers. This is because this research began in June 1980. b. Supporting units of

Education activities such as libraries, business units, LPPM, LPM and others who have used

information technology. The completeness of the data is needed so that the results of the study can

represent the condition of the UIN Sunan Ampel Surabaya university.

Independen Variable Dependen Variable

Figure 1. Construction Of Research Model

Database

Increased Information

Technology Use

Infrastructur

Brainware

Software

Classic assumption test

The first time, the data was processed and analyzed through the classic assumption test

including normality test, multicollerierity test, autocorrelation test, heteroscedasticity test and

linearity test. Autocorrelation test is performed considering the data used there are time series data

in an annual period. Testing classic assumptions must be done to test the assumptions that exist in

modeling multiple linear regression. The purpose of testing this classic assumption is to provide

certainty that the regression equations obtained have accuracy in estimation, are unbiased and

consistent. (Ghozali, 2013).

Assumption of Normality

The normality test aims to test whether the regression model, confounding or residual variables

have a normal distribution. The t test and F test assume that the residual value follows the normal

distribution. If this assumption is violated, then the statistical test becomes invalid for a small

sample size. A good regression model is to have residual values that are normally distributed. To

detect whether residuals are normally distributed or not in SPSS can use the P Plot normal test

facility, histogram test, Chi Square test, Skewness and Kurtosis test or Kolmogorov Smirnov test.

The basis of the analysis is as follows.

Assumption of Multicollinearity

Multicollinearity test aims to test whether the regression model found a correlation between

independent variables. A good regression model should not occur correlation between independent

variables. If the independent variables are correlated with each other, then these variables are not

orthogonal. Orthogonal variable is an independent variable whose correlation value between

fellow independent variables is equal to zero. To detect the presence or absence of multicollinearity

in the regression model in the SPSS program can be seen from the variance inflation factor (VIF)

in the coefficients table with a tolerance limit of no more than or equal to 10. So the assessment

criteria can be arranged as follows. IF VIF value ≤ 10, then the independent variables do not have

multicollinearity. IF VIF value ≥ 10, then the independent variables are multicollinearity.

Autocorrelation Test

Autocorrelation is one of the classic assumptions tests in multiple linear regression analysis.

Autocorrelation test is to see whether there is a correlation between a period t with the previous

period (t -1). Regression analysis aims to see the effect of the independent variables on the

dependent variable, so there should not be a correlation between observations and previous

observational data. Criteria for whether or not autocorrelation can use the Durbin Watson number

(d) and the Durbin Watson table. If (4-dL) <d <dL, then Ho is rejected, which means there is

autocorrelation. If dU <d <(4-dU), then Ho is accepted, which means there is no

autocorrelation.However, if dL <d <dU or (4-dU) <d <(4-dL), then it does not produce definitive

conclusions.

Assumption of Heteroscedasticity

Heteroscedasticity test aims to test whether in the regression model there is an inequality of

variance from the residuals of one observation to another. If the variance from one observation

residual to another observation is fixed, then it is called homoscedasticity and if different is called

heteroscedasticity. A good regression model is homoscedasticity or heteroscedasticity does not

occur. To determine the presence or absence of heteroscedasticity, it can be detected by looking at

the presence or absence of certain patterns on a scatterplot chart, the Glejser test, the Park test or

the White test. The basis of the analysis is as follows. If there are certain patterns, such as the dots

that form a regular pattern (wavy, widened and then narrowed), then it indicates that

heteroscedasticity has occurred. If there is no clear pattern, and the points spread above and below

the number 0 on the Y axis, then there is no heteroscedasticity.

Assumption Of Linearity

Linearity test is used to see whether the model built has a linear relationship or not. This test

is rarely used in various studies, because usually the model is formed based on a theoretical study

that the relationship between the independent variable and the dependent variable is linear.

Relationships between variables which in theory are not linear relationships actually can not be

analyzed by linear regression, for example the problem of elasticity. If there is a relationship

between two variables that are not yet known whether linear or not, the linearity test cannot be

used to provide an adjustment that the relationship is linear or not. The linearity test is used to

confirm whether the linear nature between the two variables identified in theory is compatible with

the results of existing observations. The linearity test can use the Means test, the Durbin-Watson

test, the Ramsey Test or the Lagrange Multiplier test.

Statistical Test F

Statistical tests to determine whether all the independent variables entered into the model have

a joint relationship with the dependent variable. The null hypothesis (Ho) that requests evaluation

is that all parameters in the model are zero, or: Ho: b1 = b2 = ...... = bk = 0, That is, all independent

variables are not a significant explanation of the dependent variable. The alternative hypothesis

(Ha) is fully approved, not all simultaneous parameters are zero, or: Ha: b1 ≠ b2 ≠ .......... ≠ bk ≠

0, Which means, all the independent variables which are significant explanations of the dependent

variable. In SPSS, the F test can be seen in the ANOVA table (Analysis of Variance) and is taken

based on the probability value (significant) compared to alpha (α) 5%. The following are the agreed

criteria. Probability If the probability (sig.)> Α (5%), then there is no significant difference

simultaneously variables X1, X2, X3, X4 and so on the variable Y, with a 95% confidence level.

If the probability (sig.) <Α (5%), then there is at least 1 variable X that is significantly significant

to the variable Y, with a confidence level of 95%.

Statistical Test t

T test shows how far the influence of one independent variable individually in explaining the

variation of the dependent variable. The null hypothesis (Ho) to be tested is whether a parameter

(bi) is equal to zero, or: Ho = bi = 0, That is, whether an independent variable is not a significant

explanation of the dependent variable. The alternative hypothesis (Ha) to be tested is that the

parameters of a variable are not equal to zero, or: Ha: bi ≠ 0, That is, the variable is a significant

explanation of the dependent variable. In SPSS, the t test can be seen in the coefficients table and

the assessment is taken based on the probability value (significance) compared to alpha (α) 5%.

The following evaluation criteria: If the probability (sig.)> Α (5%), then there is no significant

effect between the variables on the Y variable, with a confidence level of 95%. If the probability

(sig.) <Α (5%), then there is a significant influence between the variable X on the Y variable, with

a confidence level of 95%.

Determination Coefficient Test (r2)

Next, to test the suitability of a model, the coefficient of determination (r2) is used. The

coefficient of determination is the proportion of variability in a data that is calculated based on a

statistical model. The coefficient of determination is the ratio of the variability of values created

by the model with the variability of the original data values. In regression, r2 is used as a

measurement of how well the regression line approaches the original data value made by the

model. R2 can be interpreted as the proportion of variation of responses explained by the regressor

(independent variable) in the model. If r2 = 1, then the number shows the regression line matches

the data perfectly. The model fits in explaining all the variability in the Y variable. If r2 = 0, it

means that there is no relationship between the X variable and the Y variable. If 0 <r2 <1, it means

that the percent variation of the variable Y (the dependent / response variable) is explained by the

variable X (the independent / explanatory variable), while the rest is influenced by unknown

variables. Augustine and Kristaung (2013).

RESULT AND DISCUSSION

The data used in this study are companies that are included in the pure financing category that

were recorded in the annual period 1980 to 2019. All data is available to be the research sample.

Assumption of Normality

Based on the Descriptive Statistics table, the skewness ratio = -0.58 / 0.374 = -1.55 can be

calculated; while the ratio of kurtosis = 0.754 / 0.733 = 1.02. Because the skewness ratio and

kurtosis ratio are between –2 to +2, it can be concluded that the data distribution is normal.

Following Table 1. Skewness dan Kurtosis.

Table 1. Skewness and Kurtosis Descriptive Statistics

N Minimum Maximum Mean Std. Deviation Skewness Kurtosis

Statistic Statistic Statistic Statistic Statistic Statistic Std. Error

Statisti

c Std. Error

Unstandar

dized

Residual

40 -1.19809E8 9.55949E7 .0000000 4.29773928E7 -.058 .374 .754 .733

Valid N

(listwise) 40

Assumptions Of Autocorrelation

The Durbin-Watson value is 2,040 and this value will be compared with the DW table value.

The next step is to set the dl and du values, by using α = 5%, the sample (n) we have as many as

40 observations, and the independent variables as much as 4, we get the value of dl = 1.2848 and

du = 1.7209. So the DW value is smaller than the dl value (2.040> 1.2848) so it can be concluded

that this model has no positive autocorrelation symptoms. Following Table 2. Durbin-Watson Test

Results.

Table 2. Durbin-Watson Test Results

Assumptions of Multicollinearity

Based on the results of the SPSS output shows that there is no independent variable that has a

Tolerance value of less than 0.10 which means there is no correlation between the independent

variables whose value is more than 95%. The results of the calculation of the value of Variance

Inflation Factor (VIF) also showed the same thing that there was no one independent variable that

had a VIF value greater than 10. So it can be concluded that there were no symptoms of

multicollinearity between the independent variables in the regression model. Looking at the results

of the magnitude of correlation between the independent variables it appears that the database cost

variable has a low correlation with the software cost variable of -0,321, as well as the infrastructure

cost variable with Brainware costs with a correlation of -0.071 where both of these correlation

values are less than 0.70. So it can be concluded that there are no symptoms of multicollinearity

between independent variables in the regression model. Following Table 3. Tolerance and VIF

Test Results

Table 3. Tolerance and VIF Test Results

Assumption of Heteroscedasticity

The results of the SPSS output display clearly show that there are no independent variables

that are statistically significant influence the dependent variable residual absolute value (Abresid).

This can be seen from the significant probability of each independent variable, namely 0.437,

0.450, 0.491, and 0.530, all of which are greater than α = 0.05; so it can be concluded that this

regression model does not contain heteroscedasticity or homoscedasticity problems.Following

Table 4 Heteroscedasticity Abresid Test Results.

Table 4. Heteroscedasticity Abresid Test Results

Assumptions Of Linearity

Linearity test is used to see whether the model built has a linear relationship or not. If there is

a relationship between two variables that are not yet known whether linear or not, the linearity test

cannot be used to provide an adjustment that the relationship is linear or not. The linearity test is

used to confirm whether the linear nature between the two variables identified in theory is

compatible with the results of existing observations. The linearity test can use the Means test,

ANOVA, Durbin-Watson Test, Ramsey Test or Lagrange Multiplier test. Look at Table 5.

Linearity Test Results on ANOVA.

Table 5. Linearity Test Results on ANOVA

Statistical Test F (Simultaneous)

The results of Anova with probability (sig.) = 0.000 or <α (5%), then there is at least 1 variable

X which significantly influences the variable Y, with a confidence level of 95%. Following Table

6 F (Simultaneous) Statistical Test Results.

Table 6. F (Simultaneous) Statistical Test Results

Statistical Test t (Partial)

The probability result (sig.)> Α (5%), then there is no significant effect between the variables

on the Y variable, with a confidence level of 95%. Namely variable Database costs are 0.320 and

brainware costs are 0.415. This value indicates that the two variables are not significant or more

than 0.05. If the probability (sig.) <Α (5%), then there is a significant influence between the

variable X on the Y variable, with a confidence level of 95%. Namely Infrastructure costs of 0,000

and Software costs of 0,000 this value shows that the two variables are significant or less than

0.05. Following Table 7 Statistical Test Results t (Partial).

Table 7. Statistical Test Results t (Partial)

Determination Coefficient Test (r2)

The coefficient of determination test results indicate that r2 is equal to 0.779. This figure

shows that 77.9% of the variation in the Information Technology Budget Level (TATI) (dependent

/ response variable) can be explained by software cost variables, and infrastructure costs

(independent / explanatory variables), while the remaining 22.1% is influenced by variables not

known. Look this Table 8 Determination Coefficient Test Results

Table 8. Determination Coefficient Test Results

Model Summaryb

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate Durbin-Watson

1 .882a .779 .753 4.537E7 2.040

For each hypothesis the following results can be arranged with table 10.

Table 10. Resume of Hypothesis Test Results

Hypothesis Sig

Value

Supported / Not

Supported

H1: Database Cost (BD) has a negative effect on increasing the

Information technology budget (PATI)

0.320 Unsupported

H2: IT Infrastructure Costs (BITI) have a positive effect on

increasing the information technology budget

0.00 Supported

H3: The cost of Brainware (BB) has a negative effect on

increasing the information technology budget

0.415 Unsupported

H4: Software costs (BS) have a positive effect on increasing the

information technology budget

0.000 Supported

Source: researchers processed products

The regression equation is as follows:

PATI = 9,264 - 1,009BD + 3,278BITI - 0.852BB + 8.216BS + e

From the above hypothesis test it can be seen that the Cost of Database (BD) and Cost of

Brainware (BB) have a negative effect on increasing the information technology budget (PATI).

While IT Infrastructure Costs (BITI), and Software Costs (BS) simultaneously influence the

increase in the information technology budget. However, a partial test for infrastructure costs and

software costs turned out to provide significant results. That is, infrastructure costs and software

costs affect the increase in the information technology budget. This proves that the cost of

infrastructure and software costs will absorb more budget than other costs. In this research, it can

be seen that the infrastructure and software that spend the most budget increases the information

technology budget.

CONCLUSION This study aims to examine the effect of database costs, infrastructure costs, brainware costs and

software costs on the Increased Information Technology Budget. From the test results obtained that only

infrastructure and software costs have a significant effect on increasing the Information Technology

Budget. While database costs and brainware costs do not significantly influence the Information

Technology Budget Increase.

REFERENCES

Alali, Fatima., Romero, Silviana.. The use of the Internet for Corporate Reporting in The Mercosur

(Sourthern Commom Market: The Argentina Case. Advances in Accounting, Incorporating Advances

in International Accounting.pp 157-167. (2012)

Algifari. (2000). Regression Analysis. Theory, Cases & Solutions. BPFE UGM. Yogyakarta.

Anton, Howard. (2000). Elementary Linear Algebra. Fifth Edition. Drexel University. Almilia, Luciana Spica. Determining Factors of Internet Financial Reporting in Indonesia. Accounting &

Taxation. Vol. 1 (1). pp 87-99. (2009).

Aly, Doaa., Simon Jon., Hussihey Khaled. Determinants od Corporate Reporting: Evidence from Egypt.

Managerial Auditing Journal.pp 182-202. (2010)

Ambar, W.H. and Bambang, Sudibyo. "Effect of Publication of Cash Flow Reports on the Trading Volume

of Company Shares on the Jakarta Stock Exchange". Indonesian Accounting Research Journal. Vol.

1, No. 2. Pg. 239-254. 1998.

Anna, Yane Devi. Analysis of Factors Affecting Internet Corporate Reporting. 16th National Accounting

Symposium Manado. (2013).

Ashbaugh, Hollis., Johnstone, Karla M., Warfield. Terry D. (1999). Corporate Reporting on the Internet.

Horizon Accounting. Vol. 13 (3). pp 241.

Atiase, R.K. Predisclosure Information, Firm Capitalization, and Security Price Behavior Around Earnings

Announcements. Journal of Accounting Research. pp 21-36. (1985).

Augustine, Yvonne. and Kristaung, Robert. Business and Accounting Research Methodology. Dian Rakyat.

(2013).

Bandi and Hartono, Jogiyanto. Behavior of Price Reactions and Trading Volume of Stocks on Dividend

Announcements. Indonesian Accounting Research Journal. Vol.3, No. 2. Pages 203-213. (2000).

Bamber, L. Smith and Y.S. Cheon. Differential Price And Volume Reactions To Accounting Earnings

Announcement. The Accounting Review. Vol.70, No.3, pp. 417-441. (1995)

Basu Swastha. (2000). Modern Marketing Management. Eighth Edition. Liberty, Yogyakarta.

Basu Swastha and Irawan. (2000). Modern Marketing Management. Second Edition. Second

printing. Liberty Publisher, Yogyakarta. Bonson, E., T, Escobar.. A Survey on Voluntary Disclosure on the Internet. Empirical Evidence from 300

European Union Companies. The International Journal of Digital Accounting Research. Vol. 2 (1).

pp 27-51. (2002)

Bhushan, R. Collection of Information About Publicly Traded Firms: Theory and Evidence ". Journal of

Accounting and Economics 11. pp183-206. (1989)

Boyd, H. W., Walker, O. C., and Larreche, J. (2000). Marketing Management, aStrategic Approach

with a Global Orientation. Volume I. Second Edition. Publisher Erlangga, Jakarta.

Chaney, P.K. and Jeter, D.C. The Effect of Size on the Magnitude of Long-Window Earnings Response

Coefficient. Contemporary Accounting Research 8. pp.540-560. (1992)

Collins, D.W & Kothari, S.P. An Analysis of Intertemporal and Cross-Sectional Determinants of Earnings

Response Coefficients. Journal of Accounting and Economics 11. pp143-181. (1989)

Collins, D.W, Maydew, E., & Weiss, I. Changes in the Value Relevance of Earnings and Book Values

Over the Past Forty Years. Journal of Accounting and Economics. pp.39-67. (1997).

Debreceny, R., Gray, G.L., & Rahman, A. The Determinants of Internet Financial Reporting. Journal of

Accounting and Public Policy, 21, 371-394. (2002).

Desoky, Abdelmohsen M. Company characteristics as Determinants of Internet Financial Reporting in

Emerging Markets: the case of Egypt. Research in Accounting in Emerging Economies. Vol. 9. pp.

31–71. (2009).

Dhaliwal, D.S., Lee, K.J., and Fargher, N.L.. The Association Between Unexpected Earnings and Abnormal

Security Returns in the Presence of Financial Leverage. Contemporary Accounting Research, 8.

(1991)

Ettredge, M., Richardson, V.J., & Scholz, S.. Dissemination of Information for Investors at Corporate Web

Sites. Journal of Accounting and Public Policy, 21, 357-369. (2002)

Ezat, Amir., Ahmed, El-Masry. The Impact of Corporate Governance on the Timeliness of Corporate

Internet Reporting by Egyptian Listed Companies. Managerial Finance. Vol. 34 (12). pp 848-867.

(2008).

Fama, E.F. & French, K.R. The Cross-Section of Expected Stock Returns. Journal of Finance 47. pp427-

465. (1992).

Fauzan and Gunarsih. (2007). Influence of Product Attributes and Consumer Interests towards

Consumer Loyalty (Case Study on AJB Bumiputera Products 1912). Journal of Economic

Research, Business and Development, University Muhammadiyah Surakarta

Fransiska, Lusiana (2011). Differences in Stock Liquidity Before and After a Reverse Stock Split

(Empirical Study on the Indonesia Stock Exchange). Scientific Journal of Accounting and Business,

Volume 6 Number 2, July 2011.

Ghazali, Faith. (2005). Multivariate Analysis Applications with the SPPS Program, ed three,

Semarang: Diponegoro University.

Ghazali, Imam. (2006). Multivariate Analysis Using SPSS Program. Semarang: Diponegoro

University Publishing Agency.

Ghazali, Faith. (2009). Multivariate Analysis Application with SPSS Program. Mold IV. April

2009. Publishing Agency of Diponegoro University, Semarang. Ghozali, Imam. Multivariate Analysis Application with Programs. Seventh Edition. Semarang: Diponegoro

University Publisher Agency. (2013).

Harris, Milton & Raviv, Artur. Capital Sturcture and the Informational Role of Debt. The Journal of

Finance. Vol. XLV.No.2. (1990).

Healy, Paul M., & Palepu, Krishna G., Information Asymmetry, Corporate Disclosure, and the Capital

Markets: A Review of the Empirical Disclosure Literature. Journal of Accounting and Economics. 2.

pp373-381. (2001)

Imam, Shariaf. (2008). Consumer Assessment of Several Two Kinds of Attributes Sampoerna

cigarettes. http://eprints.upnjatim.ac.id/110/1/822008-97.pdf. Retrieved 12 September 2011.

Jogiyanto. (2007). Business Research Methodology. BPFE UGM. Yogyakarta.

Jogiyanto. (2007). Information Systems Research Methodology. CV. Andi Offset, Yogyakarta.

Kotler, Philip. (2000). Marketing Management, Planning Analysis, Control, Prentice Hall,

Indonesian Edition, Jakarta: Salemba 4.

Kotler, Philip. (2002), Marketing Management. Volume I. Millennium Edition. Volume I. PT.

Prenhallindo, Jakarta.

Maranatha Christian University Kotler, Philip. (2005). Marketing Management. Millennium

Edition. Volume I. PT. Prenhallindo, Jakarta.

Kotler, Philip. (2005). Marketing Management. Millennium Edition. Volume II. PT. Prenhallindo,

Jakarta.

Kotler, Philip and Gary Armstrong. (2004). Marketing Basics. Index, Jakarta.

Kotler, Philip and Keller, Kevin Lane. (2006). Marketing Management. Translation: Benyamin

Molan. Volume I. Twelfth Edition, Index publisher, Jakarta.

Kotler, Philip. Keller, Kevin Lane. (2007). Issue 12 volumes 2. Marketing Management. Jakarta:

PT INDEKS.

Kotler, Philip. Keller, Kevin Lane. (2008). Issue 12 volumes 1. Marketing Management. Jakarta:

PT INDEKS.

Kurniawan, Albert. (2009). Easy Learning SPSS For Beginners. Mediakom, Yogyakarta.

M. Iqbal Hasan. (2008). Analysis of Research Data with Statistics. PT. Earth Literacy, Jakarta.

Peter, Paul, Olson. (1996). Consumer Behavior. Consumer Behavior And Strategy Marketing.

Jakarta: Erlangga.

Peter, Paul, Olson. (1999). Consumer Behavior. Consumer Behavior And Strategy Marketing.

Jakarta: Erlangga.

Peter, J. Paul., C. Olson, Jerry. (2000). Consumer Behavior: Consumer Behavior and Marketing

strategy. Edition IV. Volume I. Erlangga, Jakarta.

Sugiyono. (2006). Business Research Methods, sixth edition, Bandung: Alfabeta.

Sugiyono. (2007). Business Research Methods, sixth edition, Bandung: Alfabeta.

Sugiyono. (2010). Quantitative Research Methods, Qualitative and R & D. Bandung: Alfabeta.

Suliyanto. (2006). Business Research Methods. ANDI. Yogyakarta.

Tjiptono, Fandy. (2008). Marketing strategy. Third Edition. Andi Publisher, Yogyakarta.

Maranatha Christian University

Setiawan, Rony et al. (2009). SPSS Training Module for Accounting Students 2009. Mananatha

Christian University. Bandung.